CFA.1                   One-factor confirmatory factor analysis model
Cor.Mat.Lomax           Correlation matrix for Lomax (1983) data set
Cor.Mat.MM              Correlation matrix for Maruyama & McGarvey
                        (1980) data set
Expected.R2             Expected value of the squared multiple
                        correlation coefficient
Gardner.LD              The Gardner learning data, which was used by
                        L.R. Tucker
HS                      Complete Data Set of Holzinger and Swineford's
                        (1939) Study
MBESS                   MBESS
Rsquare2F               Conversion functions from noncentral noncentral
                        values to their corresponding and vice versa,
                        for those related to the F-test and R Square.
Sigma.2.SigmaStar       Construct a covariance matrix with specified
                        error of approximation
Variance.R2             Variance of squared multiple correlation
                        coefficient
ancova.random.data      Generate random data for an ANCOVA model
ci.R                    Confidence interval for the multiple
                        correlation coefficient
ci.R2                   Confidence interval for the population squared
                        multiple correlation coefficient
ci.c                    Confidence interval for a contrast in a fixed
                        effects ANOVA
ci.c.ancova             Confidence interval for an (unstandardized)
                        contrast in ANCOVA with one covariate
ci.cc                   Confidence interval for the population
                        correlation coefficient
ci.cv                   Confidence interval for the coefficient of
                        variation
ci.omega2               Confidence Interval for omega-squared (omega^2)
                        for between-subject fixed-effects ANOVA and
                        ANCOVA designs (and partial omega-squared
                        omega^2_p for between-subject multifactor ANOVA
                        and ANCOVA designs)
ci.pvaf                 Confidence Interval for the Proportion of
                        Variance Accounted for (in the dependent
                        variable by knowing the levels of the factor)
ci.rc                   Confidence Interval for a Regression
                        Coefficient
ci.reg.coef             Confidence interval for a regression
                        coefficient
ci.reliability          Confidence Interval for a Reliability
                        Coefficient
ci.rmsea                Confidence interval for the population root
                        mean square error of approximation
ci.sc                   Confidence Interval for a Standardized Contrast
                        in a Fixed Effects ANOVA
ci.sc.ancova            Confidence interval for a standardized contrast
                        in ANCOVA with one covariate
ci.sm                   Confidence Interval for the Standardized Mean
ci.smd                  Confidence limits for the standardized mean
                        difference.
ci.smd.c                Confidence limits for the standardized mean
                        difference using the control group standard
                        deviation as the divisor.
ci.snr                  Confidence Interval for the Signal-To-Noise
                        Ratio
ci.src                  Confidence Interval for a Standardized
                        Regression Coefficient
ci.srsnr                Confidence Interval for the Square Root of the
                        Signal-To-Noise Ratio
conf.limits.nc.chisq    Confidence limits for noncentral chi square
                        parameters
conf.limits.ncf         Confidence limits for noncentral F parameters
conf.limits.nct         Confidence limits for a noncentrality parameter
                        from a t-distribution
cor2cov                 Correlation Matrix to Covariance Matrix
                        Conversion
covmat.from.cfm         Covariance matrix from confirmatory (single)
                        factor model.
cv                      Function to calculate the regular (which is
                        also biased) estimate of the coefficient of
                        variation or the unbiased estimate of the
                        coefficient of variation.
delta2lambda            Conversion functions for noncentral
                        t-distribution
intr.plot               Regression Surface Containing Interaction
intr.plot.2d            Plotting Conditional Regression Lines with
                        Interactions in Two Dimensions
mediation               Effect sizes and confidence intervals in a
                        mediation model
mediation.effect.bar.plot
                        Bar plots of mediation effects
mediation.effect.plot   Visualizing mediation effects
mr.cv                   Minimum risk point estimation of the population
                        coefficient of variation
mr.smd                  Minimum risk point estimation of the population
                        standardized mean difference
power.density.equivalence.md
                        Density for power of two one-sided tests
                        procedure (TOST) for equivalence
power.equivalence.md    Power of Two One-Sided Tests Procedure (TOST)
                        for Equivalence
power.equivalence.md.plot
                        Plot power of Two One-Sided Tests Procedure
                        (TOST) for Equivalence
prof.salary             Cohen et. al. (2003)'s professor salary data
                        set
s.u                     Unbiased estimate of the population standard
                        deviation
signal.to.noise.R2      Signal to noise using squared multiple
                        correlation coefficient
smd                     Standardized mean difference
smd.c                   Standardized mean difference using the control
                        group as the basis of standardization
ss.aipe.R2              Sample Size Planning for Accuracy in Parameter
                        Estimation for the multiple correlation
                        coefficient.
ss.aipe.R2.sensitivity
                        Sensitivity analysis for sample size planning
                        with the goal of Accuracy in Parameter
                        Estimation (i.e., a narrow observed confidence
                        interval)
ss.aipe.c               Sample size planning for an ANOVA contrast from
                        the Accuracy in Parameter Estimation (AIPE)
                        perspective
ss.aipe.c.ancova        Sample size planning for a contrast in
                        randomized ANCOVA from the Accuracy in
                        Parameter Estimation (AIPE) perspective
ss.aipe.c.ancova.sensitivity
                        Sensitivity analysis for sample size planning
                        for the (unstandardized) contrast in randomized
                        ANCOVA from the Accuracy in Parameter
                        Estimation (AIPE) Perspective
ss.aipe.crd.es.nclus.fixedwidth
                        Find target sample sizes for the accuracy in
                        standardized conditions means estimation in CRD
ss.aipe.crd.nclus.fixedwidth
                        Find target sample sizes for the accuracy in
                        unstandardized conditions means estimation in
                        CRD
ss.aipe.cv              Sample size planning for the coefficient of
                        variation given the goal of Accuracy in
                        Parameter Estimation approach to sample size
                        planning
ss.aipe.cv.sensitivity
                        Sensitivity analysis for sample size planning
                        given the Accuracy in Parameter Estimation
                        approach for the coefficient of variation.
ss.aipe.pcm             Sample size planning for polynomial change
                        models in longitudinal study
ss.aipe.rc              Sample size necessary for the accuracy in
                        parameter estimation approach for an
                        unstandardized regression coefficient of
                        interest
ss.aipe.rc.sensitivity
                        Sensitivity analysis for sample size planing
                        from the Accuracy in Parameter Estimation
                        Perspective for the unstandardized regression
                        coefficient
ss.aipe.reg.coef        Sample size necessary for the accuracy in
                        parameter estimation approach for a regression
                        coefficient of interest
ss.aipe.reg.coef.sensitivity
                        Sensitivity analysis for sample size planning
                        from the Accuracy in Parameter Estimation
                        Perspective for the (standardized and
                        unstandardized) regression coefficient
ss.aipe.reliability     Sample Size Planning for Accuracy in Parameter
                        Estimation for Reliability Coefficients.
ss.aipe.rmsea           Sample size planning for RMSEA in SEM
ss.aipe.rmsea.sensitivity
                        a priori Monte Carlo simulation for sample size
                        planning for RMSEA in SEM
ss.aipe.sc              Sample size planning for Accuracy in Parameter
                        Estimation (AIPE) of the standardized contrast
                        in ANOVA
ss.aipe.sc.ancova       Sample size planning from the AIPE perspective
                        for standardized ANCOVA contrasts
ss.aipe.sc.ancova.sensitivity
                        Sensitivity analysis for the sample size
                        planning method for standardized ANCOVA
                        contrast
ss.aipe.sc.sensitivity
                        Sensitivity analysis for sample size planning
                        for the standardized ANOVA contrast from the
                        Accuracy in Parameter Estimation (AIPE)
                        Perspective
ss.aipe.sem.path        Sample size planning for SEM targeted effects
ss.aipe.sem.path.sensitiv
                        a priori Monte Carlo simulation for sample size
                        planning for SEM targeted effects
ss.aipe.sm              Sample size planning for Accuracy in Parameter
                        Estimation (AIPE) of the standardized mean
ss.aipe.sm.sensitivity
                        Sensitivity analysis for sample size planning
                        for the standardized mean from the Accuracy in
                        Parameter Estimation (AIPE) Perspective
ss.aipe.smd             Sample size planning for the standardized mean
                        difference from the Accuracy in Parameter
                        Estimation (AIPE) perspective
ss.aipe.smd.lower       Sample size planning for the standardized mean
                        different from the accuracy in parameter
                        estimation approach
ss.aipe.smd.sensitivity
                        Sensitivity analysis for sample size given the
                        Accuracy in Parameter Estimation approach for
                        the standardized mean difference.
ss.aipe.src             sample size necessary for the accuracy in
                        parameter estimation approach for a
                        standardized regression coefficient of interest
ss.aipe.src.sensitivity
                        Sensitivity analysis for sample size planing
                        from the Accuracy in Parameter Estimation
                        Perspective for the standardized regression
                        coefficient
ss.power.R2             Function to plan sample size so that the test
                        of the squared multiple correlation coefficient
                        is sufficiently powerful.
ss.power.pcm            Sample size planning for power for polynomial
                        change models
ss.power.rc             sample size for a targeted regression
                        coefficient
ss.power.reg.coef       sample size for a targeted regression
                        coefficient
ss.power.sem            Sample size planning for structural equation
                        modeling from the power analysis perspective
theta.2.Sigma.theta     Compute the model-implied covariance matrix of
                        an SEM model
transform_Z.r           Transform Fischer's _Z_ into the scale of a
                        correlation coefficient
transform_r.Z           Transform a correlation coefficient (r) into
                        the scale of Fisher's Z^\prime
upsilon                 This function implements the upsilon effect
                        size statistic as described in Lachowicz,
                        Preacher, & Kelley (2018) for mediation.
var.ete                 The Variance of the Estimated Treatment Effect
                        at Selected Covariate Values in a Two-group
                        ANCOVA.
verify.ss.aipe.R2       Internal MBESS function for verifying the
                        sample size in ss.aipe.R2
vit                     Visualize individual trajectories
vit.fitted              Visualize individual trajectories with fitted
                        curve and quality of fit
